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Machine Learning, Optimization, and Data Science

5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Giuseppe Nicosia

    1. University of Cambridge, Cambridge, UK

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  2. Panos Pardalos

    1. University of Florida, Gainesville, USA

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  3. Renato Umeton

    1. Harvard University, Cambridge, USA

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  4. Giovanni Giuffrida

    1. Università di Catania, Catania, Italy

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  5. Vincenzo Sciacca

    1. Almawave, Rome, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11943)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: LOD 2019.

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Table of contents (64 papers)

  1. Front Matter

    Pages i-xxvi
    Download chapter PDF

  2. Deep Neural Network Ensembles

    • Sean Tao
    Pages 1-12

  3. Driver Distraction Detection Using Deep Neural Network

    • Shokoufeh Monjezi Kouchak, Ashraf Gaffar
    Pages 13-23

  4. Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays

    • Vittorio Mazzia, Angelo Tartaglia, Marcello Chiaberge, Dario Gandini
    Pages 24-35

  5. An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks

    • Zahra Sadeghi
    Pages 36-44

  6. Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks

    • Patrik Reizinger, Bálint Gyires-Tóth
    Pages 45-57

  7. Quantitative and Ontology-Based Comparison of Explanations for Image Classification

    • Valentina Ghidini, Alan Perotti, Rossano Schifanella
    Pages 58-70

  8. About Generative Aspects of Variational Autoencoders

    • Andrea Asperti
    Pages 71-82

  9. Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks

    • Ram B. Gurung
    Pages 83-94

  10. Incoherent Submatrix Selection via Approximate Independence Sets in Scalar Product Graphs

    • Stéphane Chrétien, Zhen Wai Olivier Ho
    Pages 95-105

  11. LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning

    • Gail Gilboa-Freedman, Alon Patelsky, Tal Sheldon
    Pages 106-117

  12. Relationship Estimation Metrics for Binary SoC Data

    • Dave McEwan, Jose Nunez-Yanez
    Pages 118-129

  13. Network Alignment Using Graphlet Signature and High Order Proximity

    • Aljohara Almulhim, Vachik S. Dave, Mohammad Al Hasan
    Pages 130-142

  14. Effect of Market Spread Over Reinforcement Learning Based Market Maker

    • Abbas Haider, Hui Wang, Bryan Scotney, Glenn Hawe
    Pages 143-153

  15. A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation

    • Marko Djukanovic, Günther R. Raidl, Christian Blum
    Pages 154-167

  16. An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem

    • Yannis Marinakis, Magdalene Marinaki
    Pages 168-179

  17. The Measure of Regular Relations Recognition Applied to the Supervised Classification Task

    • Yuriy Mikheev
    Pages 180-191

  18. Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets

    • Jamolbek Mattiev, Branko Kavšek
    Pages 192-204

  19. Analyses of Multi-collection Corpora via Compound Topic Modeling

    • Clint P. George, Wei Xia, George Michailidis
    Pages 205-218

  20. Text Mining with Constrained Tensor Decomposition

    • Elaheh Sobhani, Pierre Comon, Christian Jutten, Massoud Babaie-Zadeh
    Pages 219-231
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  1. Machine Learning, Optimization, and Data Science

Keywords

About this book

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Editors and Affiliations

  • University of Cambridge, Cambridge, UK

    Giuseppe Nicosia

  • University of Florida, Gainesville, USA

    Panos Pardalos

  • Harvard University, Cambridge, USA

    Renato Umeton

  • Università di Catania, Catania, Italy

    Giovanni Giuffrida

  • Almawave, Rome, Italy

    Vincenzo Sciacca

Bibliographic Information

  • Book Title: Machine Learning, Optimization, and Data Science

  • Book Subtitle: 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings

  • Editors: Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-37599-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-37598-0Published: 04 January 2020

  • eBook ISBN: 978-3-030-37599-7Published: 03 January 2020

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXVI, 772

  • Number of Illustrations: 65 b/w illustrations, 160 illustrations in colour

  • Topics: Information Systems Applications (incl. Internet), Artificial Intelligence, Data Mining and Knowledge Discovery

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